In offline model-based reinforcement learning (offline MBRL), we learn a dynamic model from historically collected data, and then utilize the learned model and fixed dataset for policy learning, without further interacting with the environment. Offline MBRL algorithms can improve the efficiency and stability of policy learning over the model-free based algorithms. However, in most of the existing offline MBRL algorithms, the learning objectives for the dynamic models and the policies are isolate…